A5015449063- Climate variability and models
- Oceanographic and Atmospheric Processes
- Meteorological Phenomena and Simulations
- Geophysics and Gravity Measurements
- Tropical and Extratropical Cyclones Research
- Marine and coastal ecosystems
- Atmospheric and Environmental Gas Dynamics
- Coral and Marine Ecosystems Studies
- Marine and fisheries research
- Geological and Geophysical Studies
- Climate change impacts on agriculture
- Arctic and Antarctic ice dynamics
- Hydrological Forecasting Using AI
- Crop Yield and Soil Fertility
- Science and Climate Studies
- Plant Water Relations and Carbon Dynamics
- Coastal and Marine Management
- Hydrology and Watershed Management Studies
- Plant Physiology and Cultivation Studies
- Pasture and Agricultural Systems
- Flood Risk Assessment and Management
- Data Analysis with R
- Environmental Monitoring and Data Management
- Craniofacial Disorders and Treatments
- Global Energy and Sustainability Research
Bureau of Meteorology
2013-2023
Met Office
2015-2022
University of Bedfordshire
2022
Collaboration for Australian Weather and Climate Research
2010-2015
Commonwealth Scientific and Industrial Research Organisation
2008-2015
We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier 7 two-tier systems) that participate in the Climate Prediction its Application to Society (CliPAS) project sponsored Asian-Pacific Economic Cooperation Center (APCC). also evaluated seven DEMETER models' MME for period 1981–2001 comparison. Based assessment, future direction...
Uncertainty in ocean analysis methods and deficiencies the observing system are major obstacles for reliable reconstruction of past climate. The variety existing reanalyses is exploited a multi-reanalysis ensemble to improve state estimation gauge uncertainty levels. ensemble-based signal-to-noise ratio allows identification characteristics which robust (such as tropical mixed-layer-depth, upper heat content), where large exists (deep ocean, Southern Ocean, sea ice thickness, salinity),...
Global climatic impacts of El Niño are sensitive to details the surface warming equatorial Pacific Ocean, which vary between each event. The ability predict differences in pattern anomalous ocean temperatures is explored for two prominent types Niño, traditional cold tongue events that have maximum eastern Pacific, and warm pool central Pacific. We assess seasonal predictions using Australian Bureau Meteorology coupled ocean‐atmosphere forecast model. Prediction major temperature limited...
Abstract A new ensemble ocean data assimilation system, developed for the Predictive Ocean Atmosphere Model Australia (POAMA), is described. The system called PEODAS, POAMA Ensemble Data Assimilation System. PEODAS an approximate form of Kalman filter system. For a given cycle, central forecast integrated, along with small forecasts that are forced perturbed surface fluxes. augmented multiple ensembles from previous cycles, yielding larger consists last month. This used to represent system’s...
Capture of the target, bycatch, and protected species in fisheries is often regulated through spatial measures that partition fishing effort, including areal closures. In eastern Australian waters, southern bluefin tuna (SBT, Thunnus maccoyii ) are a quota-limited multispecies longline fishery; minimizing capture by nonquota holders an important management concern. A habitat preference model (conditioned with electronic tag data) coupled ocean reanalysis data has been used since 2003 to...
Abstract The Australian Bureau of Meteorology has recently enhanced its capability to make coupled model forecasts intraseasonal climate variations. Predictive Ocean Atmosphere Model for Australia (POAMA, version 2) seasonal prediction forecast system in operations prior March 2013, designated P2-S, was not designed forecasting and deficiencies this regard. Most notably, the were only initialized on 1st 15th each month, growth ensemble spread first 30 days too slow be useful time scales....
Abstract In light of the growing recognition role surface temperature variations in Indian Ocean for driving global climate variability, predictive skill sea (SST) anomalies associated with dipole (IOD) is assessed using ensemble seasonal forecasts from a selection contemporary coupled models that are routinely used to make predictions. The authors assess predictions successive versions Australian Bureau Meteorology Predictive Ocean–Atmosphere Model Australia (POAMA 15b and 24), NCEP Climate...
ACCESS-S1 will be the next version of Australian Bureau Meteorology's seasonal prediction system, due to become operational in early 2018. The multiweek and performance has been evaluated based on a 23-year hindcast set compared current POAMA. system considerable enhancements POAMA, including higher vertical horizontal resolution component models state-ofthe-art physics parameterisation schemes. is UK Met Office GloSea5-GC2 but ensemble generation strategy make it appropriate for multi-week...
Multi-year La Niña events often induce persistent cool and wet climate over global lands, altering in some case mitigating regional warming impacts. The latest event lingered from mid-2010 to early 2012 brought about intensive precipitation many land regions of the world, particularly Australia. This resulted a significant drop mean sea level despite background upwards trend. is surprisingly predicted out two years ahead few coupled models, even though predictability El Niño-Southern...
ACCESS-S2 is a major upgrade to the Australian Bureau of Meteorology’s multi-week seasonal prediction system. It was made operational in October 2021, replacing ACCESS-S1. The focus addition new weakly coupled data assimilation system provide initial conditions for atmosphere, ocean, land and ice fields. model based on UK Met Office GloSea5-GC2 unchanged from ACCESS-S1, aside minor corrections enhancements. performance skill forecasts have been assessed compared There are improvements...
Abstract This study examines the potential use of Predictive Ocean Atmosphere Model for Australia (POAMA), Bureau Meteorology's dynamical seasonal forecast system, as an intraseasonal prediction tool Australia. would fill current capability gap between weather forecasts and outlooks The skill a 27‐year hindcast dataset is investigated, focusing on precipitation minimum maximum temperatures over in second fortnight (average days 15–28 forecast). Most forecasting temperature focused eastern...
The potential for climate predictability at seasonal time scales resides in information provided by the ocean initial conditions, particular upper thermal structure. Currently, several operational centres issue routine forecasts produced with coupled ocean-atmosphere models, requiring real-time knowledge of state global ocean. Seasonal forecasting needs calibration numerical output model, which turn requires an historical reanalysis, as will be discussed this paper. Assimilation observations...
We quantify seasonal prediction skill of tropical winter rainfall in 14 climate forecast systems. High levels exist for year‐to‐year variability all ocean basins. The East Pacific is the most skilful region, with very high correlation scores, and West also highly skilful. Predictions Atlantic Indian Ocean show lower but statistically significant scores. compare (measured against observed variability) model predictability (using single forecasts as surrogate observations). Model matches some...
Abstract Predictive skill for El Niño in the equatorial eastern Pacific across a range of forecast models declined sharply early twenty-first century relative to what was achieved late twentieth despite ongoing improvements systems. This decline coincided with shift climate an enhanced east–west surface temperature gradient and stronger Walker circulation at end century. Using seasonal sensitivity experiments Australian Bureau Meteorology coupled model POAMA2.4, authors show that this...
Abstract The impact of ocean data assimilation on dynamical El Niño–Southern Oscillation forecasting is studied by looking at forecasts started from initial conditions produced with and without assimilation. Sensitivity further examined comparing coupled obtained using different ocean‐forcing fields. A total four reanalyses for the period 1990–97, two analyses sub‐surface only wind sea‐surface‐temperature (SST) information are considered. Different wind‐stress forcing produces significantly...
Abstract The evolution of the Indian Ocean during El Niño–Southern Oscillation is investigated in a 100-yr integration an Australian Bureau Meteorology coupled seasonal forecast model. During Niño, easterly anomalies are induced across eastern equatorial Ocean. These act to suppress thermocline west and elevate it east initially cool (warm) sea surface temperature (SST) (west). Subsequently, entire basin warms, mainly response reduced latent heat flux enhanced shortwave radiation that...